# Import the pandas, pandas_profiling and seaborn libraries
import pandas as pd
import pandas_profiling
import seaborn as sns
# Import the “bi_product.txt” and the “bi_salesFact.txt” files
product=pd.read_table("/Users/Nicolas/Desktop/Masterstudium /Semester 3/Ljubljana/Big Data Management/Datasets/Python/bi_product.txt", sep=";")
fact=pd.read_table("/Users/Nicolas/Desktop/Masterstudium /Semester 3/Ljubljana/Big Data Management/Datasets/Python/bi_salesFact.txt", sep=";")
# Checking if both tables are successfully read
fact
# Checking if both tables are successfully read
product
# Merging the tables (product and fact) with pandas to create a table “total”
total= pd.merge(product, fact, on=["ProductID"])
# Checking if the tables were successfully merged
total
# Getting an overview of the data with profile_report()
total.profile_report()